Papers hide their weak spots behind confident prose. This prompt forces every claim to show its evidence, or admit it has none. A Reddit user posted it on r/PromptEngineering, and it’s one of the cleanest source-checking prompts I’ve seen.
The normal way to summarize a paper gives you a tidy paragraph. Tidy paragraphs are the problem. They smooth over the gap between “the data shows X” and “the author believes X.” That gap is exactly what you need to see before you trust a claim.
What the Prompt Actually Does
The original poster, u/Hungry-Fudge2281, built something that splits every claim in a text from the evidence behind it. Instead of one smooth summary, you get a table: each claim on its own row, next to what actually backs it up.
Here’s the breakdown of how it works:
- It pulls out every claim in plain, one-sentence language.
- For each one, it checks what evidence the text offers: data, a citation, an example, or nothing at all.
- It rates the strength as strong, moderate, weak, or “asserted only.”
- It notes what would make the claim stronger.
That “asserted only” category is the one worth paying attention to. This creator built it specifically to catch confident writing with no real backing behind it. Confident tone and strong evidence are not the same thing, and most summarizing tools blur that line completely without meaning to.
After the table, the prompt asks the model to flag any claim with zero support and note anything the text implies but never says outright. Then it names the single strongest and weakest claim in the piece. One commenter tested it on a short article with deliberately mismatched claims and evidence, and it caught every gap.
Why This Works
Two design choices are doing the heavy lifting here.
First, splitting claim from evidence is an old critical-reading habit, just handed to a model that can run it fast across a long document. You’d normally do this by hand with a highlighter and a lot of patience. This prompt does it in one pass, on a paper of any length.
Second, and this is the part that actually matters: the line “do not infer evidence that is not in the text.” Skip that instruction and models tend to fill gaps with plausible-sounding support the source never gave. Ask “what backs this claim” without it, and you’ll often get a citation-shaped answer instead of an honest “none.” One sentence is doing more work here than the rest of the prompt combined.
Use Cases
This isn’t limited to academic papers. Try it on:
- 📄 Research papers before you cite them in your own writing
- 📰 News articles making bold claims on thin sourcing
- 📊 Internal reports or pitch decks where “the data supports this” needs a gut check
- Long threads on LinkedIn or Twitter dressed up as analysis
Anywhere confident writing might be hiding a thin argument, this prompt earns its keep. It works just as well on a five-paragraph blog post as it does on a full research paper.
Prompt of the Day
Paste this with the text you want to pressure-test:
Analyze the text below. I want to see what it claims versus what it actually supports.
Text:
“””
[PASTE PAPER OR ARTICLE]
“””Output a table with these columns:
- Claim (one sentence, in plain language)
- Evidence given (what the text offers: data, citation, example, or nothing)
- Strength (strong / moderate / weak / asserted only)
- What would make it stronger
After the table:
- List any claim that has no supporting evidence.
- Note anything the text implies but never states outright.
- One line: what is the strongest claim here, and what is the weakest.
Do not infer evidence that is not in the text. If there is none, say “none.”
Want to push it further? Swap the source text for a competitor’s marketing page and run the same table on their sales claims instead. Or run it on the same paper again in six months and check whether new evidence has shown up to back the weak spots.
Worth Trying
Run this on the next paper or article someone sends you before you take its conclusion at face value. It takes one pass, and it’ll show you exactly where the argument gets thin. Check out the original thread on r/PromptEngineering for the full discussion and more replies from other prompt builders.
Frequently Asked Questions
Q: Why does the prompt sometimes create redundant rows?
The model can over-split related statements into separate claims, for example, turning “Productivity increased 12%” and “therefore remote work works” into three rows instead of one. This is actually useful because the redundancy usually reveals where observation got inflated into causation. When you spot duplicates, look at which ones the model flagged as “asserted only”, that’s where the weak support hides.
Q: Is the compound-sentence splitting part of the prompt, or just a lucky side effect?
It’s an emergent behavior, the model splits compound sentences on its own, even though the prompt doesn’t explicitly ask for it. This makes the prompt powerful for catching causal leaps, but also unpredictable. If you need more reliable splitting, explicitly add: “Separate observation-based claims from causal claims.”
Q: What if the full table feels like overkill?
A lighter alternative: ask the model to name one alternative explanation for each major finding before it commits to a conclusion. It catches single-cause bias faster and is cheaper to run, useful if you’re just spot-checking a few key claims instead of auditing the whole paper.
A research prompt that separates claims from evidence so you can see what a paper actually proves
by u/Hungry-Fudge2281 in PromptEngineering